To address this question we build on our previous work that outlined a framework for imbuing Deep RL algorithms with a hippocampal learning system [1]}. The approach, termed Complementary Temporal Difference Learning (CTDL), was inspired by the theory of Complementary Learning Systems (CLS) [2]}. CLS theory states that the brain relies upon the complementary properties of the neocortex and the hippocampus to perform complex behaviour. In particular, the neocortex gradually learns overlapping representations from multiple experiences (i.e. semantic memory), while the hippocampus rapidly learns pattern-separated representations of individual experiences (i.e. episodic memory). As a result, these two learning systems allow for both a high-degree of generalization and rapid learning with minimal interference, which are both critical for efficient Reinforcement Learning (RL) behaviour [3]}, [4]}. CTDL provides support for this theory by demonstrating that the performance of classic Deep Reinforcement Learning (RL) algorithms can be improved by imbuing them with two learning systems that mimic the key properties of the neocortex and the hippocampus (also see [5]}, [6]}).
